Anomalous object interaction detection and reporting
First Claim
1. A computer-implemented method for analyzing a scene depicted in an input stream of video frames captured by a video camera, the method comprising:
- receiving at least two sequences, wherein each sequence of the at least two sequences corresponds to a segment of a trajectory taken by a respective object through the scene;
determining, via one or more processors, whether the objects interact based on a spatio-temporal proximity of the objects; and
if the objects interact;
mapping each sequence of the at least two sequences to a respective sequence cluster,retrieving, from an ngram trie, a learned joint probability indicating a likelihood of a given sequence cluster pair of a plurality of sequence cluster pairs occurring in the scene, the ngram trie including a plurality of nodes, the given sequence cluster pair being represented jointly by a first node in a first layer of the ngram trie and a second node in a second layer of the ngram trie, the first node and the second node representing sequence clusters in the given sequence cluster pair, anddetermining a rareness value for each sequence cluster pair based on the learned joint probability and a frequency of a most frequently observed sequence cluster pair from the scene, the rareness value given by
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Abstract
Techniques are disclosed for analyzing a scene depicted in an input stream of video frames captured by a video camera. The techniques include evaluating sequence pairs representing segments of object trajectories. Assuming the objects interact, each of the sequences of the sequence pair may be mapped to a sequence cluster of an adaptive resonance theory (ART) network. A rareness value for the pair of sequence clusters may be determined based on learned joint probabilities of sequence cluster pairs. A statistical anomaly model, which may be specific to an interaction type or general to a plurality of interaction types, is used to determine an anomaly temperature, and alerts are issued based at least on the anomaly temperature. In addition, the ART network and the statistical anomaly model are updated based on the current interaction.
61 Citations
23 Claims
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1. A computer-implemented method for analyzing a scene depicted in an input stream of video frames captured by a video camera, the method comprising:
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receiving at least two sequences, wherein each sequence of the at least two sequences corresponds to a segment of a trajectory taken by a respective object through the scene; determining, via one or more processors, whether the objects interact based on a spatio-temporal proximity of the objects; and if the objects interact; mapping each sequence of the at least two sequences to a respective sequence cluster, retrieving, from an ngram trie, a learned joint probability indicating a likelihood of a given sequence cluster pair of a plurality of sequence cluster pairs occurring in the scene, the ngram trie including a plurality of nodes, the given sequence cluster pair being represented jointly by a first node in a first layer of the ngram trie and a second node in a second layer of the ngram trie, the first node and the second node representing sequence clusters in the given sequence cluster pair, and determining a rareness value for each sequence cluster pair based on the learned joint probability and a frequency of a most frequently observed sequence cluster pair from the scene, the rareness value given by - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9)
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10. A non-transitory computer-readable storage medium storing instructions, which when executed by a computer system, perform operations for analyzing a scene depicted in an input stream of video frames captured by a video camera, the instructions comprising instructions to:
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receive at least two sequences, each sequence of the at least two sequences corresponding to a segment of a trajectory taken by a respective object through the scene; determine, via one or more processors, whether the objects interact based on a spatio-temporal proximity of the objects; and if the objects interact; map each sequence of the at least two sequences to a sequence cluster, retrieve, from an ngram trie, a learned joint probability indicating a likelihood of a given sequence cluster pair from a plurality of sequence cluster pairs occurring in the scene, the ngram trie including a plurality of nodes, the (liven sequence cluster pair being represented jointly by a first node in a first layer of the ngram trie and a second node in a second layer of the ngram trie, the first node and the second node representing sequence clusters in the (liven sequence cluster pair, and determine a rareness value for each sequence cluster pair of the plurality of sequence cluster pairs based on learned joint probabilities of sequence cluster pairs and a frequency of a most frequently observed sequence cluster pair, the rareness value based on - View Dependent Claims (11, 12, 13, 14, 15, 16, 17, 18)
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19. A system, comprising:
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a processor; and a memory, wherein the memory includes an application program configured to perform operations for analyzing a scene depicted in an input stream of video frames captured by a video camera, the operations comprising; receiving at least two sequences, wherein each sequence of the at least two sequences corresponds to a segment of a trajectory taken by a respective object through the scene, determining, via one or more processors, whether the objects interact based on a spatio-temporal proximity of the objects, and if the objects interact; mapping each sequence of the at least two sequences to a sequence cluster; retrieving, from an ngram trie, learned joint probabilities of sequence cluster pairs, a learned joint probability indicating a likelihood of a sequence cluster pair occurring in the scene, the ngram trie including a plurality of nodes, the sequence cluster pair being represented jointly by a first node in a first layer of the ngram trie and a second node in a second layer of the ngram trie, the first node and the second node representing sequence clusters in the sequence cluster pair, and determining a rareness value based on the learned joint probabilities and a frequency of a most frequently observed sequence cluster pair, the rareness value based on - View Dependent Claims (20, 21, 22, 23)
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Specification